Currently, in navigation between pages on the web, a user encounters a large amount of data. There are many cases in which the user would like to generate queries for further searching based on the data she captures during this navigation.
Current methods enable either a “copy and paste” action to send copied data to a search engine or text highlighting and sending to search via mouse right-click. Neither of these methods preserves the structure or context of the data being captured.
An example is a case where the user captures a portion of a table and would like to search for a similar portion being available on the web, i.e. an element that appears in a table context.
Assume a user looks at a table/figure that lists laboratory results, as follows:
MeasureValuemeasure1Highmeasure2Lowmeasure35measure4Averagemeasure533 mmHg
A search request which does not take context into account would search for instances with the relevant words (measure1, measure2 . . . , High, Low, . . . ).
In order to enable the user to retrieve similar instances, the context which associates “measure1” with the value “High”, “measure2” with the value “Low”, etc. is needed.
There are cases in which the data to be searched does not have available HTML (HyperText Markup Language) source (for example, Adobe Flex-based applications (Adobe and Adobe Flex are trade marks of Adobe Systems Inc.). There are also cases in which the user cannot easily select the text in the page, for example, if text is part of an image.
There are other cases in which the HTML source is available, such as in a web browser viewing a simple HTML web page, and similar HTML instances need to be found that were indexed on the web.